{"id":"https://openalex.org/W7134809834","doi":"https://doi.org/10.48550/arxiv.2603.08531","title":"CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning","display_name":"CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7134809834","doi":"https://doi.org/10.48550/arxiv.2603.08531"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.08531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08531","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.08531","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128631777","display_name":"Yi-Shiuan Tung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tung, Yi-Shiuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004964249","display_name":"Gyanig Kumar","orcid":"https://orcid.org/0000-0001-9045-2630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Gyanig","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128666017","display_name":"Wei Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128655662","display_name":"Bradley Hayes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hayes, Bradley","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020277024","display_name":"Alessandro Roncone","orcid":"https://orcid.org/0000-0001-7385-1875"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roncone, Alessandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.29330000281333923,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.29330000281333923,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.12870000302791595,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.08060000091791153,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.8123000264167786},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7078999876976013},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6140999794006348},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5697000026702881},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5611000061035156},{"id":"https://openalex.org/keywords/preference-learning","display_name":"Preference learning","score":0.5307999849319458},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5049999952316284}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8123000264167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7258999943733215},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7078999876976013},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6140999794006348},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5611000061035156},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5493000149726868},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.5307999849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5054000020027161},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4097000062465668},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.33340001106262207},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2809000015258789},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.26739999651908875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.08531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08531","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.08531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.08531","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.5035746693611145,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"a":[1,24,98,169],"robot's":[2],"operational":[3],"environment":[4,112,130],"and":[5,17,49,88,114,120,132,145,168,182,185],"tasks":[6],"to":[7,22,41,45,51,91,117,155],"perform":[8],"within":[9],"it":[10],"grow":[11],"in":[12,179],"complexity,":[13],"the":[14,151],"explicit":[15],"specification":[16],"balancing":[18],"of":[19,32],"optimization":[20],"objectives":[21],"achieve":[23],"preferred":[25],"behavior":[26,44],"profile":[27],"moves":[28],"increasingly":[29],"farther":[30],"out":[31],"reach.":[33],"These":[34],"systems":[35],"benefit":[36],"strongly":[37],"by":[38,68,109,137],"being":[39],"able":[40],"align":[42],"their":[43],"reflect":[46],"human":[47,65],"preferences":[48,122],"respond":[50],"corrections,":[52],"but":[53],"manually":[54],"encoding":[55],"this":[56,149],"feedback":[57],"is":[58],"infeasible.":[59],"Active":[60],"preference":[61],"learning":[62],"(APL)":[63],"learns":[64],"reward":[66,107,164,180],"functions":[67],"presenting":[69],"trajectories":[70],"for":[71,103],"ranking.":[72],"However,":[73],"existing":[74],"methods":[75,178],"sample":[76,183],"from":[77,123,141],"fixed":[78],"trajectory":[79,100,115,157],"sets":[80],"or":[81],"replay":[82],"buffers":[83],"that":[84,105,159,173],"limit":[85],"query":[86,119],"diversity":[87],"often":[89],"fail":[90],"identify":[92],"informative":[93],"comparisons.":[94],"We":[95],"propose":[96],"CRED,":[97],"novel":[99],"generation":[101],"method":[102],"APL":[104],"improves":[106],"inference":[108],"jointly":[110],"optimizing":[111],"design":[113,131],"selection":[116],"efficiently":[118],"extract":[121],"users.":[124],"CRED":[125,174],"\"imagines\"":[126],"new":[127],"scenarios":[128],"through":[129],"leverages":[133],"counterfactual":[134],"reasoning":[135],"--":[136,154],"sampling":[138],"possible":[139],"rewards":[140],"its":[142],"current":[143],"belief":[144],"asking":[146],"\"What":[147],"if":[148],"were":[150],"true":[152],"preference?\"":[153],"generate":[156],"pairs":[158],"expose":[160],"differences":[161],"between":[162],"competing":[163],"functions.":[165],"Comprehensive":[166],"experiments":[167],"user":[170,188],"study":[171],"show":[172],"significantly":[175],"outperforms":[176],"state-of-the-art":[177],"accuracy":[181],"efficiency":[184],"receives":[186],"higher":[187],"ratings.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-11T00:00:00"}